1.6 has been released! this has been in the works for over 6 months now and i'm very excited to finally release it. this is a massive balanc
super-big update i've been working on for the past 6 months, testing with multiple campaigns i was part of and multiple i wasn't - the dozens of players in my community have given me so much more helpful feedback than i ever would've been able to gather by myself just running for my home group.
me and my co-designer @turntechz have such a better understanding of the game by now - how people actually play, how the game's previously untested subsystems work out in practice. and this update is just applying all of that experience and testing so everything runs buttery-smooth
we did big sweeps through both books to polish stuff left from early versions when i didn't understand the game as well. all the most underwhelming perks, spells and monsters were brought in line with more viable options. lots of classes got big changes to their core abilities so they do a much better job justifying their existence and standing out in a game with 25 classes.
anyway you can go read the more detailed changelogs on the itch page or in the book but the bottom line is the game's more fun. no real big new features to announce cause those are done! all that was left was more testing and polishing and perfecting the game, and that's what this is. it's basically done, just go play it!
on top of the big update last week, paying customers now have early access to the game's second core book! at the moment it's not even close to done, but it has four new classes and a couple other goodies. it's less tested than the stuff in the core book, but still perfectly playable.
if you've paid at least $6 for tactiquest already, you have access to the new book - you don't have to pay again.
Version 1.6.1 is up and contains a handful of small patches (particularly to Oracle, which had a bit of leftover jank that needed smoothing
im posting this here despite the website being extremely white centered, I want people to understand how in this country it's basically ok to murder and victimize black people, especially women and children in the name of "self defense" and white America will reward you for your antiblackness.
i think we should be ridiculing them more for this. you don't get to try and go all "queer website" when your staff likes to go on nuking sprees targeting the trans fem users
would be remiss not to mention that the rainbow notably straight up just removed the trans flag colors from it. like they’re gone. it’s the progress flag minus the trans flag colors.
i think we should be ridiculing them more for this. you don't get to try and go all "queer website" when your staff likes to go on nuking sprees targeting the trans fem users
Text of tweet under the cut because it is loooong.
But... Stochastic Parrots.
Timnit Gebru was fired from Google in December 2020 for refusing to retract a research paper, and every single warning that paper made about large language models has now happened at a scale the industry spent 4 years trying to make people forget about.
Her name is Timnit Gebru.
She co-led the Ethical AI team at Google. She co-wrote a paper called "On the Dangers of Stochastic Parrots" with Emily Bender at the University of Washington and two other researchers. The paper was 14 pages long. It was submitted to a top AI ethics conference. And it was the reason Google decided that one of the most senior Black women in AI research could no longer work there.
The story Google told publicly was that she resigned. The story she told, confirmed by 2,695 of her colleagues in an open letter, was that she was fired by email while on vacation because she refused to either retract the paper or remove her name from it.
The paper had not even been published yet.
Here is what she actually wrote, and why every prediction inside it has now come true.
The first warning was about scale itself. Bender and Gebru argued that training ever-larger models on ever-larger scrapes of the internet would produce systems that appeared fluent but had no actual understanding of language. They called these systems stochastic parrots because they would repeat patterns from training data with statistical confidence and zero comprehension. The paper predicted that this apparent intelligence would fool both users and developers into trusting outputs that were structurally incapable of being reliable.
This was 2020. GPT-3 had just come out. The paper predicted the hallucination problem before anyone had a word for it.
The second warning was about bias amplification. The paper documented in detail that internet-scale training data contains systematic overrepresentation of dominant viewpoints and underrepresentation of marginalized ones. The models would not just absorb this bias. They would amplify it, because the optimization process rewards confident outputs, and confidence in language patterns tracks frequency in the training set.
The prediction was that hiring tools built on these models would discriminate against women. That healthcare triage tools would underperform on Black patients. That loan approval systems would entrench inequality while presenting their decisions as neutral algorithmic judgment.
Every one of those things has now been documented in deployment.
Amazon's hiring algorithm penalized resumes that contained the word "women" in any context. Healthcare risk scoring algorithms used by major US hospitals were found to systematically underestimate the medical needs of Black patients. Apple Card's credit algorithm gave wives credit lines 10x lower than their husbands for the same financial profile.
The third warning was about environmental cost. The paper calculated that training a single large language model produced emissions equivalent to the lifetime output of 5 cars. The prediction was that the race to scale would create an environmental footprint that would eventually rival entire industries.
In 2024, Google's emissions were up 48% from 2019, and the company explicitly blamed AI infrastructure. Microsoft's were up 29%, same reason. Both companies have now quietly abandoned the climate commitments they were publicly celebrating the year Gebru was fired.
The fourth warning was about documentation. The paper argued that the training datasets being assembled were too large for anyone to actually audit. Nobody at Google, OpenAI, Meta, or any other lab could tell you with confidence what was in the data their models were trained on. This was not a temporary problem to be solved later. It was a permanent feature of the approach.
In 2023, researchers discovered that the LAION-5B dataset, used to train Stable Diffusion and other major image models, contained thousands of images of child sexual abuse material. The companies that had trained on the dataset had no way of knowing. The paper predicted that category of failure 3 years before it was found.
The fifth warning was the one Google cared about most.
Bender and Gebru argued that the deployment of these systems would centralize linguistic and cultural power in the hands of the small number of companies that could afford to train them. The internet would become a place where the dominant voice was a statistical average of dominant voices, presented as a neutral assistant. Languages underrepresented in the training data would degrade over time as more web content was generated by these systems and fed back into the next training run.
This is now happening in real time. A 2024 study found that 57% of new web content in English is AI-generated or AI-assisted. Researchers studying low-resource languages have documented active degradation in translation quality, because the synthetic content fed back into training is itself worse in those languages.
The paper Google fired her for predicted the model collapse problem before model collapse had a name.
The mechanism behind why this all happened is the part of her work that nobody quotes.
Gebru's argument was not that AI is dangerous in some abstract sci-fi sense. Her argument was that AI is dangerous in a very specific structural sense. The technology was being built by a small group of researchers who shared similar backgrounds, worked at similar companies, and were rewarded for shipping products faster than competitors. The incentive structure made it impossible for safety, ethics, and bias concerns to slow anything down. Anyone inside the system who raised those concerns was either ignored, sidelined, or removed.
She was making that argument from inside Google.
Then Google proved her right by removing her.
The team Google had built to make sure their AI was safe was dismantled in 90 days because they did the job they had been hired to do. Margaret Mitchell, the other co-lead of the Ethical AI team, was fired two months after Gebru for searching through her own emails for evidence of how Gebru had been treated.
Gebru did not stop. She founded DAIR, the Distributed AI Research Institute, in 2021. The mission is to do AI research outside the control of the companies that have a financial interest in not hearing the answers.
Every prediction in the Stochastic Parrots paper has now been validated by deployment. Hallucinations are an industry-wide problem the largest labs cannot solve. Bias amplification has been documented in hiring, healthcare, lending, and criminal justice. Environmental costs are larger than entire small countries. Training data audits remain impossible. Model collapse is an active research crisis at every major lab.
The question worth sitting with is the one almost no one in the industry will say out loud.
Every researcher with the technical credibility to call out these problems watched what happened to her in December 2020 and made a calculation about their own career. The number of people willing to speak publicly about safety and ethics issues inside the major AI labs collapsed after that firing and has not recovered.
The researcher Google fired for warning about exactly what is now happening was right.
The company that fired her is now the second-largest deployer of the technology she warned about.
And the people inside that company who agree with her are not allowed to say so.
The cops very clearly planted evidence on him because they had to make an arrest because all eyes were on them and whoever actually did the deed was making them look stupid.
Why would the real killer hero have kept the weapon on his person and traveled two states over while carrying it and a manifesto in his bag, conveniently turning the crime into a federal matter? The same guy whose bag they found in a park, filled with monopoly money? Why did the police turn off their bodycams, take Luigi's stuff, drive a block away, turn their bodycams back on, go back into the restaurant, and then arrest him?
From the moment of his arrest, even left-of-center media has been presuming his guilt without examining anything (e.g. calling him "the killer" instead of "alleged" or "accused") and then when I say he didn't do it, the nearest person chimes in with some quip that tells me they think he did do it but should go free anyway. Don't get me wrong, I would have the same attitude if he had done it. But he didn't. It makes me feel like the only sane person in the world, even among my staunchly leftist friends.
Protect Internet Freedom from now until forever. It's important existentially! Americans stand with UK citizens in our struggle against government censorship
You guys have my whole heart for sharing this I had no idea will be filling this out and encourage all my fellow brits to do soo too. If you’re not from the UK please keep sharing this around we have till the 26th May to submit these in.
This whole thing was set up without our say we all need to make sure we’re heard.
level2janitor @level2janitor - Tumblr Blog | Tumgag
